Predictive Modelling of Student Outcomes Using Ensemble Regression and Classification Methods

Mary Teresa; Sukerthi Sutraya; Y Vijaya Sambhavi; Saritha Dasari; J K Neelima

doi:10.5281/zenodo.17599070

Authors

Mary Teresa Department of CSE-AIML, Guru Nanak Institutions Technical Campus, Hyderabad, India
Sukerthi Sutraya Department of Computer Science and Engineering (Data Science), G. Narayanamma Institute of Technology and Science, Hyderabad, India.
Y Vijaya Sambhavi Department of EEE, Annamacharya Institute of Technology and Sciences (Autonomous), Tirupati, India.
Saritha Dasari Department of Computer Science and Engineering (Data Science), G. Narayanamma Institute of Technology and Science, Hyderabad, India.
J K Neelima Department of E.C.E, Narayana Engineering College, Nellore, India.

DOI:

https://doi.org/10.5281/zenodo.17599070

Keywords:

Student Performance Prediction, Ensemble Learning, HistGradientBoostingClassifier, Educational Data Mining, Multiclass Classification, Machine Learning

Abstract

Accurate prediction of student academic outcomes is vital for developing data-driven interventions in education. This study proposes a robust ensemble learning framework based on the HistGradientBoostingClassifier (HGB) to classify student grades using behavioral and academic features such as self-study hours, attendance, class participation, and total performance scores. Leveraging a large-scale synthetic dataset of 1,000,000 student records, we benchmarked the proposed HGB model against widely used ensemble classifiers including XGBoost, LightGBM, CatBoost, and Random Forest. Comprehensive experiments demonstrated that HGB consistently outperformed all baselines, achieving a testing accuracy of 99.6%, with macro-averaged precision, recall, and F1-score of 0.99. The model also showed strong generalization across both majority and minority grade categories, as confirmed by confusion matrix analysis. These results highlight the effectiveness of histogram-based boosting in educational data mining and support its application in real-time academic performance monitoring and intervention systems.

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